The study investigated the sero-status of human immunodeficiency virus among healthcare workers in Addis Ababa public hospitals. A multi-centered, institutional-based, cross-sectional study was conducted from 18 September 2022 to 30 October 2022. A simple random sampling method and a semi-structured, self-administered questionnaire were used to collect the data, which were analyzed using the Statistical Package for Social Sciences (SPSS) version 25. A binary logistic regression model was used to identify the factors associated with the human immunodeficiency virus sero-status of healthcare workers post exposure to infected blood and body fluids. Of the 420 study participants who were exposed to blood and body fluids, 403 (96%) were non-reactive. Healthcare workers who had 20–29 years of work experience had approximately six times higher odds of testing positive for the human immunodeficiency virus (AOR = 6.21, 95% CI: 2.39, 9.55). Healthcare workers who did not use personal protective equipment properly had five times higher odds of testing positive for the human immunodeficiency virus (AOR = 5.02, CI: 3.73, 9.51). This study showed that, among those healthcare workers who tested positive for the human immunodeficiency virus infection, the majority were from the emergency department. Healthcare workers who did not use personal protective equipment properly had higher odds of testing positive for the human immunodeficiency virus.

Burn patients are at high risk of central line–associated bloodstream infection (CLABSI). However, the diagnosis of such infections is complex, resource-intensive, and often delayed. This study aimed to investigate the epidemiology of CLABSI and develop a prediction model for the infection in burn patients. The study analysed the infection profiles, clinical epidemiology, and central venous catheter (CVC) management of patients in a large burn centre in China from January 2018 to December 2021. In total, 222 burn patients with a cumulative 630 CVCs and 5,431 line-days were included. The CLABSI rate was 23.02 CVCs per 1000 line-days. The three most common bacterial species were Acinetobacter baumannii, Staphylococcus aureus, and Pseudomonas aeruginosa; 76.09% of isolates were multidrug resistant. Compared with a non-CLABSI cohort, CLABSI patients were significantly older, with more severe burns, more CVC insertion times, and longer total line-days, as well as higher mortality. Regression analysis found longer line-days, more catheterisation times, and higher burn wounds index to be independent risk factors for CLABSI. A novel nomogram based on three risk factors was constructed with an area under the receiver operating characteristic curve (AUROC) value of 0.84 (95% CI: 0.782–0.898) with a mean absolute error of calibration curve of 0.023. The nomogram showed excellent predictive ability and clinical applicability, and provided a simple, practical, and quantitative strategy to predict CLABSI in burn patients.

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

We consider a branching random walk on a d-ary tree of height n ($n \in \mathbb{N}$), in the presence of a hard wall which restricts each value to be positive, where d is a natural number satisfying $d\geqslant2$. We consider the behaviour of Gaussian processes with long-range interactions, for example the discrete Gaussian free field, under the condition that it is positive on a large subset of vertices. We observe a relation with the expected maximum of the processes. We find the probability of the event that the branching random walk is positive at every vertex in the nth generation, and show that the conditional expectation of the Gaussian variable at a typical vertex, under positivity, is less than the expected maximum by order of $\log n$.

Toxigenic diphtheria is rare in Australia with generally fewer than 10 cases reported annually; however, since 2020, there has been an increase in toxin gene-bearing isolates of Corynebacterium diphtheriae cases in North Queensland, with an approximately 300% escalation in cases in 2022. Genomic analysis on both toxin gene-bearing and non-toxin gene-bearing C. diphtheriae isolated from this region between 2017 and 2022 demonstrated that the surge in cases was largely due to one sequence type (ST), ST381, all of which carried the toxin gene. ST381 isolates collected between 2020 and 2022 were highly genetically related to each other, and less closely related to ST381 isolates collected prior to 2020. The most common ST in non-toxin gene-bearing isolates from North Queensland was ST39, an ST that has also been increasing in numbers since 2018. Phylogenetic analysis demonstrated that ST381 isolates were not closely related to any of the non-toxin gene-bearing isolates collected from this region, suggesting that the increase in toxigenic C. diphtheriae is likely due to the expansion of a toxin gene-bearing clone that has moved into the region rather than an already endemic non-toxigenic strain acquiring the toxin gene.

Let ${\mathrm{d}} X(t) = -Y(t) \, {\mathrm{d}} t$, where Y(t) is a one-dimensional diffusion process, and let $\tau(x,y)$ be the first time the process (X(t), Y(t)), starting from (x, y), leaves a subset of the first quadrant. The problem of computing the probability $p(x,y)\,:\!=\, \mathbb{P}[X(\tau(x,y))=0]$ is considered. The Laplace transform of the function p(x, y) is obtained in important particular cases, and it is shown that the transform can at least be inverted numerically. Explicit expressions for the Laplace transform of $\mathbb{E}[\tau(x,y)]$ and of the moment-generating function of $\tau(x,y)$ can also be derived.

The exponential growth of data collection opens possibilities for analyzing data to address political and societal challenges. Still, European cities are not utilizing the potential of data generated by its citizens, industries, academia, and public authorities for their public service mission. The reasons are complex and relate to an intertwined set of organizational, technological, and legal barriers, although good practices exist that could be scaled, sustained, and further developed. The article contributes to research on data-driven innovation in the public sector comparing high-level expectations on data ecosystems with actual practices of data sharing and innovation at the local and regional level. Our approach consists in triangulating the analysis of in-depth interviews with representatives of the local administrations with documents obtained from the cities. The interviews investigated the experiences and perspectives of local administrations regarding establishing a local or regional data ecosystem. The article examines experiences and obstacles to data sharing within seven administrations investigating what currently prevents the establishment of data ecosystems. The findings are summarized along three main lines. First, the limited involvement of private sector organizations as actors in local data ecosystems through emerging forms of data sharing became evident. Second, we observed the concern over technological aspects and the lack of attention on social or organizational issues. Third, a conceptual decision to apply a centralized and not a federated digital infrastructure is noteworthy.

The exploitation of hydrocarbon reservoirs may potentially lead to contamination of soils, shallow water resources, and greenhouse gas emissions. Fluids such as methane or CO2 may in some cases migrate toward the groundwater zone and atmosphere through and along imperfectly sealed hydrocarbon wells. Field tests in hydrocarbon-producing regions are routinely conducted for detecting serious leakage to prevent environmental pollution. The challenge is that testing is costly, time-consuming, and sometimes labor-intensive. In this study, machine learning approaches were applied to predict serious leakage with uncertainty quantification for wells that have not been field tested in Alberta, Canada. An improved imputation technique was developed by Cholesky factorization of the covariance matrix between features, where missing data are imputed via conditioning of available values. The uncertainty in imputed values was quantified and incorporated into the final prediction to improve decision-making. Next, a wide range of predictive algorithms and various performance metrics were considered to achieve the most reliable classifier. However, a highly skewed distribution of field tests toward the negative class (nonserious leakage) forces predictive models to unrealistically underestimate the minority class (serious leakage). To address this issue, a combination of oversampling, undersampling, and ensemble learning was applied. By investigating all the models on never-before-seen data, an optimum classifier with minimal false negative prediction was determined. The developed methodology can be applied to identify the wells with the highest likelihood for serious fluid leakage within producing fields. This information is of key importance for optimizing field test operations to achieve economic and environmental benefits.

In this paper we consider the problem of averaging for a class of piecewise deterministic Markov processes (PDMPs) whose dynamic is constrained by the presence of a boundary. On reaching the boundary, the process is forced to jump away from it. We assume that this boundary is attractive for the process in question in the sense that its averaged flow is not tangent to it. Our averaging result relies strongly on the existence of densities for the process, allowing us to study the average number of crossings of a smooth hypersurface by an unconstrained PDMP and to deduce from this study averaging results for constrained PDMPs.

This article analyzes raw driving data of passenger cars in the city of Semnan in Iran, with the objective of understanding the impact of traffic conditions at different times of day (morning, noon, evening, and night). For this study, two cars, the Toyota Prius and the Peugeot Pars (or the IKCO Persia), were used, and the data of speed, longitude, latitude, and altitude of the vehicles were acquired. This data was collected over a week (July 21–28, 2022) for a distance of 670 km (13 hr), with the help of the Global Positioning System application, and were presented for both cars. In addition to this, the data on fuel consumption and average speed, based on the Electronic Control Unit in the Prius, was also collected. Finally, a sensitivity analysis was done on the features of the raw data, based on the Principal Component Analysis method.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We’ve used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

To develop a machine learning model and nomogram to predict the probability of persistent virus shedding (PVS) in hospitalized patients with coronavirus disease 2019 (COVID-19), the clinical symptoms and signs, laboratory parameters, cytokines, and immune cell data of 429 patients with nonsevere COVID-19 were retrospectively reviewed. Two models were developed using the Akaike information criterion (AIC). The performance of these two models was analyzed and compared by the receiver operating characteristic (ROC) curve, calibration curve, net reclassification index (NRI), and integrated discrimination improvement (IDI). The final model included the following independent predictors of PVS: sex, C-reactive protein (CRP) level, interleukin-6 (IL-6) level, the neutrophil-lymphocyte ratio (NLR), monocyte count (MC), albumin (ALB) level, and serum potassium level. The model performed well in both the internal validation (corrected C-statistic = 0.748, corrected Brier score = 0.201) and external validation datasets (corrected C-statistic = 0.793, corrected Brier score = 0.190). The internal calibration was very good (corrected slope = 0.910). The model developed in this study showed high discriminant performance in predicting PVS in nonsevere COVID-19 patients. Because of the availability and accessibility of the model, the nomogram designed in this study could provide a useful prognostic tool for clinicians and medical decision-makers.

We propose a new Kalikow decomposition for continuous-time multivariate counting processes, on potentially infinite networks. We prove the existence of such a decomposition in various cases. This decomposition allows us to derive simulation algorithms that hold either for stationary processes with potentially infinite network but bounded intensities, or for processes with unbounded intensities in a finite network and with empty past before zero. The Kalikow decomposition is not unique, and we discuss the choice of the decomposition in terms of algorithmic efficiency in certain cases. We apply these methods to several examples: the linear Hawkes process, the age-dependent Hawkes process, the exponential Hawkes process, and the Galves–Löcherbach process.

Suppose that a system is affected by a sequence of random shocks that occur over certain time periods. In this paper we study the discrete censored $\delta$-shock model, $\delta \ge 1$, for which the system fails whenever no shock occurs within a $\delta$-length time period from the last shock, by supposing that the interarrival times between consecutive shocks are described by a first-order Markov chain (as well as under the binomial shock process, i.e., when the interarrival times between successive shocks have a geometric distribution). Using the Markov chain embedding technique introduced by Chadjiconstantinidis et al. (Adv. Appl. Prob. 32, 2000), we study the joint and marginal distributions of the system’s lifetime, the number of shocks, and the number of periods in which no shocks occur, up to the failure of the system. The joint and marginal probability generating functions of these random variables are obtained, and several recursions and exact formulae are given for the evaluation of their probability mass functions and moments. It is shown that the system’s lifetime follows a Markov geometric distribution of order $\delta$ (a geometric distribution of order $\delta$ under the binomial setup) and also that it follows a matrix-geometric distribution. Some reliability properties are also given under the binomial shock process, by showing that a shift of the system’s lifetime random variable follows a compound geometric distribution. Finally, we introduce a new mixed discrete censored $\delta$-shock model, for which the system fails when no shock occurs within a $\delta$-length time period from the last shock, or the magnitude of the shock is larger than a given critical threshold $\gamma >0$. Similarly, for this mixed model, we study the joint and marginal distributions of the system’s lifetime, the number of shocks, and the number of periods in which no shocks occur, up to the failure of the system, under the binomial shock process.

We propose a test for anticipated changes in spot volatility, either due to continuous or discontinuous price moves, at the times of realization of event risk in the form of pre-scheduled releases of economic information such as earnings announcements by firms and macroeconomic news announcements. These events can generate nontrivial volatility in asset returns, which does not scale even locally in time. Our test is based on short-dated options written on an underlying asset subject to event risk, which takes place after the options’ observation time and prior to or after their expiration. We use options with different tenors to estimate the conditional (risk-neutral) characteristic functions of the underlying asset log-returns over the horizons of the options. Using these estimates and a relationship between the conditional characteristic functions with three different tenors, which holds true if and only if continuous and discontinuous spot volatility does not change at the event time, we design a test for this hypothesis. In an empirical application, we study anticipated individual stocks’ volatility changes following earnings announcements for a set of stocks with good option coverage.

This study aimed to determine the impact of current hepatitis B virus (HBV) infection on patients hospitalised with sepsis. This was a retrospective cohort study. Patients from three medical centres in Suzhou from 10 January 2016 to 23 July 2022 participated in this study. Demographic characteristics and clinical characteristics were collected. A total of 945 adult patients with sepsis were included. The median age was 66.0 years, 68.6% were male, 13.1% presented with current HBV infection, and 34.9% of all patients died. In the multivariable-adjusted Cox model, patients with current HBV infection had significantly higher mortality than those without (hazard ratio (HR) 1.50, 95% confidence interval (CI) 1.11–2.02). A subgroup analysis showed that being infected with HBV significantly increased in-hospital mortality in patients younger than 65 years old (HR 1.74, 95% CI 1.16–2.63), whereas no significant impact was observed in patients ≥65 years. The propensity score-matched case–control analysis showed that the rate of septic shock (91.4% vs. 62.1%, P < 0.001) and in-hospital mortality (48.3% vs. 35.3%, P = 0.045) were much higher in the propensity score-matched HBV infection group compared with the control group. In conclusion, current HBV infection was associated with mortality in adults with sepsis.

We investigate jointly modelling age–year-specific rates of various causes of death in a multinational setting. We apply multi-output Gaussian processes (MOGPs), a spatial machine learning method, to smooth and extrapolate multiple cause-of-death mortality rates across several countries and both genders. To maintain flexibility and scalability, we investigate MOGPs with Kronecker-structured kernels and latent factors. In particular, we develop a custom multi-level MOGP that leverages the gridded structure of mortality tables to efficiently capture heterogeneity and dependence across different factor inputs. Results are illustrated with datasets from the Human Cause-of-Death Database (HCD). We discuss a case study involving cancer variations in three European nations and a US-based study that considers eight top-level causes and includes comparison to all-cause analysis. Our models provide insights into the commonality of cause-specific mortality trends and demonstrate the opportunities for respective data fusion.

We study the problem of determining the minimum number $f(n,k,d)$ of affine subspaces of codimension $d$ that are required to cover all points of $\mathbb{F}_2^n\setminus \{\vec{0}\}$ at least $k$ times while covering the origin at most $k - 1$ times. The case $k=1$ is a classic result of Jamison, which was independently obtained by Brouwer and Schrijver for $d = 1$. The value of $f(n,1,1)$ also follows from a well-known theorem of Alon and Füredi about coverings of finite grids in affine spaces over arbitrary fields. Here we determine the value of this function exactly in various ranges of the parameters. In particular, we prove that for $k\geq 2^{n-d-1}$ we have $f(n,k,d)=2^d k-\left\lfloor{\frac{k}{2^{n-d}}}\right\rfloor$, while for $n \gt 2^{2^d k-k-d+1}$ we have $f(n,k,d)=n + 2^d k -d-2$, and obtain asymptotic results between these two ranges. While previous work in this direction has primarily employed the polynomial method, we prove our results through more direct combinatorial and probabilistic arguments, and also exploit a connection to coding theory.